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Analysis of parallel algorithms using pipeline architectures in computer vision applications

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Abstract

Recently a number of machine vision systems have been successfully implemented using pipeline architectures and various new algorithms have been proposed. In this paper we propose a method of analysis of both time complexity and space complexity for algorithms using conventional general purpose pipeline architectures. We illustrate our method by applying it to an algorithm schema for local window operations satisfying a property we define as decomposability. It is shown that the proposed algorithm schema and its analysis generalize previous published results. We further analyse algorithms implementing operators that are not decomposable. In particular the complexities of several median-type operations are compared and the implication on algorithm choice is discussed. We conclude with discussions on space-time trade-offs and implementation issues.

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References

  1. E. Ataman, V.K. Aatre and K.M. Wong, Some statistical properties of median filters, IEEE Trans. Acoust., Speech, Signal Proc. ASSP-29 (1981) 1073–1075.

    Google Scholar 

  2. G.R. Arce and R.J. Crinon, Median filters: Analysis for 2-dimensional recursively filtered signals, in:Proc. IEEE ICASSP-84, San Diego, CA (Mar. 1984) pp. 20.11.1–20.11.4.

  3. G.R. Arce and N.C. Gallagher, State description for root-signal set of median filters, IEEE Trans. Acoust., Speech, Signal Proc. ASSP-30 (1982) 894–902.

    Google Scholar 

  4. G.R. Arce and M.P. McLauglin, Theoretical analysis of the max/med filter, IEEE Trans. Acoust., Speech, Signal Proc. ASSP-35 (1987) 60–69.

    Google Scholar 

  5. A.C. Bovik, Streaking in median filtered images, IEEE Trans. Acoust., Speech, Signal Proc. ASSP-35 (1987) 493–503.

    Google Scholar 

  6. A. Basu and C.M. Brown, Algorithms and hardware for efficient image smoothing, Computer Vision, Graphics and Image Proc. 40 (1987) 131–146.

    Google Scholar 

  7. A.C. Bovik, T.S. Huang and D.C. Munson, Jr., A generalization of median filtering using linear combinations of order statistics, IEEE Trans. Acoust., Speech, Signal Proc. ASSP-31 (1983) 1342–1350.

    Google Scholar 

  8. A.C. Bovik, T.S. Huang and D.C. Munson, Jr., Effect of median filtering on edge estimation, IEEE Trans. Pattern Anal. Machine Int. PAMI-9 (1987) 181–194.

    Google Scholar 

  9. R.T. Chin, Algorithms and techniques for automated visual inspection, in:Handbook of Pattern Recognition and Image Processing, eds. T.Y. Young and K.S. Fu (Academic Press, 1986) pp. 587–612.

  10. R. Cypher and J.L. Sanz, SIMD mesh array algorithms for image component labeling, IBM Technical Report, Almaden Research Centre, San Jose, CA (Feb. 1987).

  11. H. Derin and H. Elliot, Modeling and segmentation of noisy and textures images using Gibbs random fields, IEEE Trans. Pattern Anal. Machine Int. PAMI-9 (1987) 39–55.

    Google Scholar 

  12. I. Dinstein and A.C. Fong (Lochovsky), Computing local minima and maxima of digital images in pipeline image processing systems equipped with hardware comparators, Proc. IEEE (1988) 286–287.

  13. P.E. Danielson and S. Levialdi, Computer architectures for pictorial information processing, IEEE Computer (Nov. 1981) 53–67.

  14. M.J.B. Duff, CLIP4: A large scale integrated circuit array parallel processor,Proc. 3rd Int. Conf. on Pattern Recognition (1976) pp. 723–728.

  15. A. Fong (Lochovsky), Computation of a class of detail preserving filters in pipeline architectures,Proc. Vision Interface '88, Canadian Image Processing and Pattern Recognition Society, Edmonton (June, 1988) pp. 92–97.

  16. A. Fong (Lochovsky), Increasing software productivity of pipeline image processing architectures in manufacture inspection applications,IEEE 1988 Int. Conf. on Systems, Man and Cybernetics, Beijing, China (Aug., 1988) pp. 1142–1145.

  17. A. Fong (Lochovsky), Algorithms and architectures for a class of non-linear hybrid filters using pipeline architectures, Computer Vision, Graphics and Image Processing 50 (1990) 101–111.

    Google Scholar 

  18. A. Fong, The effect of window mask shape on algorithm design in pipeline architectures, in:Proc. Vision Interface '89, Canadian Image Processing and Pattern Recognition Society, London, Ont. (June, 1989) pp. 81–87.

  19. A. Fong, Exploiting window mask shape in algorithm design in pipeline architectures, Technical Report CIS89D3, Dept. of Computing and Information Science, University of Guelph (July, 1989).

  20. T.J. Fountain, A survey of bit-serial processor circuits, in:Computer Structures for Image Processing, ed. M.J.B. Duff (Academic Press, London, 1983).

    Google Scholar 

  21. T.J. Fountain, Array architectures for iconic and symbolic image processing,Proc. 8th Int. Conf. on Pattern Recognition, Paris (Oct., 1986).

  22. T. Gross, H.T. Kung, M. Lam and J. Webb, WARP as a machine for low-level vision,Proc. IEEE Int. Conf. on Robotics and Automation (Mar., 1985).

  23. N.C. Gallagher, Jr., and G.L. Wise, A theoretical analysis of the properties of median filters, IEEE Trans. Acoust., Speech, Signal Proc. ASSP-29 (1981) 1136–1141.

    Google Scholar 

  24. T.S. Huang (ed.),Two-Dimensional Digital Signal Processing. II: Transforms and Median Filters (Springer, New York, 1981).

    Google Scholar 

  25. D. Hillis,The Connection Machine (MIT Press, Cambridge, MA, 1985).

    Google Scholar 

  26. K. Hwang, J. Ghish and R. Chow, Computer architecture for artificial intelligence processing, IEEE Computer (Jan., 1987).

  27. S. Horowitz and T. Pavlidis, Picture segmentation by a tree traversal algorithms, J. ACM 23 (1976).

  28. E. Hinkle, J. Sanz, A. Jain and D. Petkovic, PPPE: New-life for projection-based image processing, J. Parallel Distr. Comp. 4 (1987) 45–78.

    Google Scholar 

  29. F. Kuhlman and G.L. Wise, On second moment properties of median filtered sequences of independent data, IEEE Trans. Commun. COM-29 (1981) 1374–1379.

    Google Scholar 

  30. P.M. Narendra, A separable median filter for image noise smoothing, IEEE Trans Pattern Anal. Machine Int. PAMI-3 (1981).

  31. T.A. Nodes and N.C. Gallagher, Jr., Two-dimensional root structures and convergence properties of the separable median filter, IEEE Trans. Acoust., Speech, Signal Proc. ASSP-31 (1983) 1350–1365.

    Google Scholar 

  32. T.A. Nodes and N.C. Gallagher, Jr., The output distribution of median type filters, IEEE Trans. Commun. COM-32 (1984) 532–541.

    Google Scholar 

  33. A. Nieminen, P. Heinonen and Y. Neuvo, A new class of detail-preserving filters for image processing, IEEE Trans. Pattern Anal. Machine Int. PAMI-9 (1987) 74–90.

    Google Scholar 

  34. E. Persoon, A pipelined image analysis system using customed circuits, IEEE Trans. Pattern Anal. Machine Int. PAMI-10 (1988) 110–117 and 74–90.

    Google Scholar 

  35. D. Petkovic et al., An experimental system for disc heads inspection,Proc. 8th Int. Conf. on Pattern Recognition, Paris, France (Oct., 1986).

  36. A.P. Reeves, Survey: Parallel computer architectures for image processing, Computer Vision, Graphics, and Image Proc. 25 (1984) 68–88.

    Google Scholar 

  37. E.R. Ritenour, T.R. Nelson and U. Raff, Applications of the median filter to digital radiographic images, in:Proc. IEEE ICASSP-84, San Diego, CA (Mar. 1984) pp. 23.1.1–23.1.4.

  38. H.J. Siegel et al., PASM: A partitionable SIMD/MIMD system for image processing and pattern recognition, IEEE Trans. Comp. C-30 (1981).

  39. J.L.C. Sanz and M.D. Flickner, Computing minima and maxima of digital images in pipeline image processing systems without hardware comparators, Proc. IEEE 72, no. 8 (Aug., 1985) 1333–1334.

    Google Scholar 

  40. J.L.C. Sanz, A new method for computing polygonal marks in image processing pipeline architectures, Pattern Recognition, 18, nos. 3/4 (1985) 241–247.

    Google Scholar 

  41. J.L.C. Sanz and I. Dinstein, Projection-based geometrical feature extraction for computer vision algorithms: algorithms in pipeline architectures, IEEE Trans. Pattern Anal. Machine Int. PAMI-9 (1987) 160–168.

    Google Scholar 

  42. J.L. Sanz and E. Hinkle, Computing projections in pipeline architectures, IEEE Trans. Acoust., Speech, Signal Proc. ASSP-35 (1987) 198–207.

    Google Scholar 

  43. J.L.C. Sanz, I. Dinstein and D. Petkovic, A new procedure for computing multi-colored polygonal masks in image processing pipeline architectures and its application to automatic visual inspection, CACM (April, 1987) 318–329, in:Proc. IEEE ISCAS-85, Kyoto, Japan (1985) pp. 1331–1334.

  44. J.L.C. Sanz, E. Hinkle, and I. Dinstein, A new approach to computing geometric features of digital objects for machine vision, image processing and image analysis: algorithms in pipeline architectures, in:CVPR 85, San Francisco, CA (June 1985).

  45. J.L.C. Sanz and D. Petkovic, Machine vision algorithms for automated inspection of thin-film disk heads, IEEE Trans. Pattern Anal. Machine Int. PAMI-10 (1988) 830–848.

    Google Scholar 

  46. J.W. Tukey, Nonlinear (nonsuperposable) methods for smoothing data, in:Conf. Rec., EASCON (1974) p. 673.

  47. L. Uhr, Pyramid multi-computer structures, and augmented pyramids, in:Computing Structures for Image Processing, ed. M.J.B. Duff (Academic Press, London, 1983).

    Google Scholar 

  48. L. Uhr, Parallel architectures for image processing, computer vision, and pattern perception, in:Handbook of Pattern Recognition and Image Processing, eds. Young and K.S. Fu (Academic Press, 1986).

  49. B. Wah and G. Li,Computers for Artificial Intelligence Applications (IEEE Tutorial, ISBN-O-8186-0706-8, May, 1986).

  50. B.H. McCormick, The Illinois pattern recognition computer — ILLIAC III, IEEE Trans. Comput. C-13 (1963) 791–813.

    Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science, The Hong Kong University of Science and Technology, Clear Water Bay Rd., Tai Po Tsai, Kowloon, Hong Kong

    Amelia Fong Lochovsky

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  1. Amelia Fong Lochovsky
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Additional information

This research was partially supported by a grant from the Natural Science and Engineering Research Council of Canada. Part of this work was done while the author was at the University of Guelph, Guelph, Ontario, Canada.

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Fong Lochovsky, A. Analysis of parallel algorithms using pipeline architectures in computer vision applications. Ann Math Artif Intell 4, 177–209 (1991). https://doi.org/10.1007/BF01531178

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